Overview

Dataset statistics

Number of variables13
Number of observations3144
Missing cells312
Missing cells (%)0.8%
Duplicate rows23
Duplicate rows (%)0.7%
Total size in memory788.7 KiB
Average record size in memory256.9 B

Variable types

NUM11
CAT2

Warnings

Dataset has 23 (0.7%) duplicate rows Duplicates
Project has a high cardinality: 520 distinct values High cardinality
Est FY16 is highly correlated with TotalHigh correlation
Total is highly correlated with Est FY16 and 6 other fieldsHigh correlation
6 Year Total is highly correlated with Total and 5 other fieldsHigh correlation
FY 17 is highly correlated with Total and 3 other fieldsHigh correlation
FY 18 is highly correlated with Total and 4 other fieldsHigh correlation
FY 19 is highly correlated with Total and 5 other fieldsHigh correlation
FY 20 is highly correlated with Total and 4 other fieldsHigh correlation
FY 21 is highly correlated with Total and 4 other fieldsHigh correlation
FY 22 is highly correlated with FY 21High correlation
Project is uniformly distributed Uniform
Cost Element is uniformly distributed Uniform
Total has 1045 (33.2%) zeros Zeros
Thru FY15 has 1831 (58.2%) zeros Zeros
Est FY16 has 1736 (55.2%) zeros Zeros
6 Year Total has 1777 (56.5%) zeros Zeros
FY 17 has 2014 (64.1%) zeros Zeros
FY 18 has 2066 (65.7%) zeros Zeros
FY 19 has 2176 (69.2%) zeros Zeros
FY 20 has 2292 (72.9%) zeros Zeros
FY 21 has 2384 (75.8%) zeros Zeros
FY 22 has 2454 (78.1%) zeros Zeros
Beyond 6 Yrs has 2929 (93.2%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:57:46.224035
Analysis finished2020-12-13 00:57:59.924825
Duration13.7 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Project
Categorical

HIGH CARDINALITY
UNIFORM

Distinct520
Distinct (%)16.7%
Missing24
Missing (%)0.8%
Memory size24.7 KiB
Libraries
 
6
Chapman Avenue Extended (P500719)
 
6
Wyngate ES Addition (P116513)
 
6
Good Hope Neighborhood Recreation Center (P720918)
 
6
Glen Echo Fire Station Renovation (P450702)
 
6
Other values (515)
3090 
ValueCountFrequency (%) 
Libraries60.2%
 
Chapman Avenue Extended (P500719)60.2%
 
Wyngate ES Addition (P116513)60.2%
 
Good Hope Neighborhood Recreation Center (P720918)60.2%
 
Glen Echo Fire Station Renovation (P450702)60.2%
 
MCPS Funding Reconciliation (P076510)60.2%
 
Public Facilities Roads (P507310)60.2%
 
Potomac WFP Consent Decree Program (P173801)60.2%
 
Clarksburg HS Addition (P116505)60.2%
 
Westbrook ES Addition (P116512)60.2%
 
Clarksburg Cluster ES (Clarksburg Village Site #1) (P116504)60.2%
 
General Government60.2%
 
Neighborhood Traffic Calming (P509523)60.2%
 
Enterprise Facilities' Improvements (P998773)60.2%
 
School Based Health & Linkages to Learning Centers (P640400)60.2%
 
Facility Planning: SM (P809319)60.2%
 
Traffic Improvements60.2%
 
Rapid Transit System (P501318)60.2%
 
Public Safety60.2%
 
Integrated Justice Information System (P340200)60.2%
 
Council Office Building Garage (P011601)60.2%
 
Energy Conservation: MCPS (P796222)60.2%
 
Community Development60.2%
 
Water Montgomery County60.2%
 
Universities at Shady Grove Expansion (P151201)60.2%
 
Other values (495)297094.5%
 
(Missing)240.8%
 
2020-12-12T19:58:00.012902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:58:00.113488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length79
Median length38
Mean length38.3740458
Min length3

Overview of Unicode Properties

Unique unicode characters72
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1210810.0%
 
e83947.0%
 
n63905.3%
 
i60545.0%
 
a60005.0%
 
t59104.9%
 
o56044.6%
 
r52144.3%
 
P43143.6%
 
040263.3%
 
l33362.8%
 
s32342.7%
 
(29402.4%
 
)29402.4%
 
127122.2%
 
d25322.1%
 
c24182.0%
 
522801.9%
 
S18061.5%
 
u16981.4%
 
m16621.4%
 
C15601.3%
 
g14581.2%
 
714581.2%
 
614461.2%
 
Other values (47)2315419.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6744055.9%
 
Decimal Number1729214.3%
 
Uppercase Letter1668613.8%
 
Space Separator1210810.0%
 
Open Punctuation29402.4%
 
Close Punctuation29402.4%
 
Other Punctuation8700.7%
 
Dash Punctuation3720.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P431425.9%
 
S180610.8%
 
C15609.3%
 
R12727.6%
 
A9005.4%
 
M8465.1%
 
B6724.0%
 
E6604.0%
 
F6183.7%
 
W5823.5%
 
L5343.2%
 
T4983.0%
 
D4682.8%
 
G4322.6%
 
I3362.0%
 
H3061.8%
 
N3001.8%
 
O2401.4%
 
U1080.6%
 
V900.5%
 
J600.4%
 
K360.2%
 
Q300.2%
 
Z120.1%
 
Y6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e839412.4%
 
n63909.5%
 
i60549.0%
 
a60008.9%
 
t59108.8%
 
o56048.3%
 
r52147.7%
 
l33364.9%
 
s32344.8%
 
d25323.8%
 
c24183.6%
 
u16982.5%
 
m16622.5%
 
g14582.2%
 
p12361.8%
 
y11881.8%
 
h10621.6%
 
v10501.6%
 
k8821.3%
 
f6601.0%
 
b5520.8%
 
w4860.7%
 
x1500.2%
 
q1080.2%
 
z1020.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
12108100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(2940100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0402623.3%
 
1271215.7%
 
5228013.2%
 
714588.4%
 
614468.4%
 
313387.7%
 
812067.0%
 
910085.8%
 
29545.5%
 
48645.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)2940100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-372100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:43249.7%
 
/12614.5%
 
&12013.8%
 
.9010.3%
 
,667.6%
 
#303.4%
 
'60.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8412669.7%
 
Common3652230.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e839410.0%
 
n63907.6%
 
i60547.2%
 
a60007.1%
 
t59107.0%
 
o56046.7%
 
r52146.2%
 
P43145.1%
 
l33364.0%
 
s32343.8%
 
d25323.0%
 
c24182.9%
 
S18062.1%
 
u16982.0%
 
m16622.0%
 
C15601.9%
 
g14581.7%
 
R12721.5%
 
p12361.5%
 
y11881.4%
 
h10621.3%
 
v10501.2%
 
A9001.1%
 
k8821.0%
 
M8461.0%
 
Other values (26)81069.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
1210833.2%
 
0402611.0%
 
(29408.0%
 
)29408.0%
 
127127.4%
 
522806.2%
 
714584.0%
 
614464.0%
 
313383.7%
 
812063.3%
 
910082.8%
 
29542.6%
 
48642.4%
 
:4321.2%
 
-3721.0%
 
/1260.3%
 
&1200.3%
 
.900.2%
 
,660.2%
 
#300.1%
 
'6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII120648100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1210810.0%
 
e83947.0%
 
n63905.3%
 
i60545.0%
 
a60005.0%
 
t59104.9%
 
o56044.6%
 
r52144.3%
 
P43143.6%
 
040263.3%
 
l33362.8%
 
s32342.7%
 
(29402.4%
 
)29402.4%
 
127122.2%
 
d25322.1%
 
c24182.0%
 
522801.9%
 
S18061.5%
 
u16981.4%
 
m16621.4%
 
C15601.3%
 
g14581.2%
 
714581.2%
 
614461.2%
 
Other values (47)2315419.2%
 

Cost Element
Categorical

UNIFORM

Distinct6
Distinct (%)0.2%
Missing24
Missing (%)0.8%
Memory size24.7 KiB
Other
520 
Planning, Design and Supervision
520 
Total Expenditures
520 
Construction
520 
Site Improvements and Utilities
520 
ValueCountFrequency (%) 
Other52016.5%
 
Planning, Design and Supervision52016.5%
 
Total Expenditures52016.5%
 
Construction52016.5%
 
Site Improvements and Utilities52016.5%
 
Land52016.5%
 
(Missing)240.8%
 
2020-12-12T19:58:00.202565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:58:00.250106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:58:00.318164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length12
Mean length16.89312977
Min length3

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n628811.8%
 
i52009.8%
 
e46808.8%
 
t46808.8%
 
36406.9%
 
s31205.9%
 
a26244.9%
 
r26004.9%
 
o26004.9%
 
d20803.9%
 
l15602.9%
 
u15602.9%
 
p15602.9%
 
g10402.0%
 
S10402.0%
 
v10402.0%
 
m10402.0%
 
P5201.0%
 
,5201.0%
 
D5201.0%
 
L5201.0%
 
I5201.0%
 
U5201.0%
 
C5201.0%
 
c5201.0%
 
Other values (5)26004.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4323281.4%
 
Uppercase Letter572010.8%
 
Space Separator36406.9%
 
Other Punctuation5201.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S104018.2%
 
P5209.1%
 
D5209.1%
 
L5209.1%
 
I5209.1%
 
U5209.1%
 
C5209.1%
 
O5209.1%
 
T5209.1%
 
E5209.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n628814.5%
 
i520012.0%
 
e468010.8%
 
t468010.8%
 
s31207.2%
 
a26246.1%
 
r26006.0%
 
o26006.0%
 
d20804.8%
 
l15603.6%
 
u15603.6%
 
p15603.6%
 
g10402.4%
 
v10402.4%
 
m10402.4%
 
c5201.2%
 
h5201.2%
 
x5201.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,520100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3640100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4895292.2%
 
Common41607.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n628812.8%
 
i520010.6%
 
e46809.6%
 
t46809.6%
 
s31206.4%
 
a26245.4%
 
r26005.3%
 
o26005.3%
 
d20804.2%
 
l15603.2%
 
u15603.2%
 
p15603.2%
 
g10402.1%
 
S10402.1%
 
v10402.1%
 
m10402.1%
 
P5201.1%
 
D5201.1%
 
L5201.1%
 
I5201.1%
 
U5201.1%
 
C5201.1%
 
c5201.1%
 
O5201.1%
 
h5201.1%
 
Other values (3)15603.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
364087.5%
 
,52012.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53112100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n628811.8%
 
i52009.8%
 
e46808.8%
 
t46808.8%
 
36406.9%
 
s31205.9%
 
a26244.9%
 
r26004.9%
 
o26004.9%
 
d20803.9%
 
l15602.9%
 
u15602.9%
 
p15602.9%
 
g10402.0%
 
S10402.0%
 
v10402.0%
 
m10402.0%
 
P5201.0%
 
,5201.0%
 
D5201.0%
 
L5201.0%
 
I5201.0%
 
U5201.0%
 
C5201.0%
 
c5201.0%
 
Other values (5)26004.9%
 

Total
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1670
Distinct (%)53.5%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean16240.20192
Minimum-1522
Maximum1848877
Zeros1045
Zeros (%)33.2%
Memory size24.7 KiB
2020-12-12T19:58:00.395731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1522
5-th percentile0
Q10
median618
Q35698.75
95-th percentile66548
Maximum1848877
Range1850399
Interquartile range (IQR)5698.75

Descriptive statistics

Standard deviation80384.14639
Coefficient of variation (CV)4.949701165
Kurtosis211.6527076
Mean16240.20192
Median Absolute Deviation (MAD)618
Skewness12.753861
Sum50669430
Variance6461610990
MonotocityNot monotonic
2020-12-12T19:58:00.490813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0104533.2%
 
2170.5%
 
1160.5%
 
4100.3%
 
200100.3%
 
380.3%
 
40080.3%
 
2070.2%
 
100060.2%
 
35050.2%
 
1050.2%
 
950.2%
 
4450.2%
 
300040.1%
 
640.1%
 
540.1%
 
2240.1%
 
50040.1%
 
10040.1%
 
22740.1%
 
190040.1%
 
2540.1%
 
24040.1%
 
1540.1%
 
740.1%
 
Other values (1645)192561.2%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-15221< 0.1%
 
0104533.2%
 
1160.5%
 
2170.5%
 
380.3%
 
4100.3%
 
540.1%
 
640.1%
 
740.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
18488771< 0.1%
 
14957651< 0.1%
 
14355801< 0.1%
 
13098101< 0.1%
 
11825141< 0.1%
 
9754411< 0.1%
 
8704671< 0.1%
 
8234201< 0.1%
 
7900601< 0.1%
 
7195641< 0.1%
 

Thru FY15
Real number (ℝ≥0)

ZEROS

Distinct949
Distinct (%)30.4%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3889.716667
Minimum0
Maximum462612
Zeros1831
Zeros (%)58.2%
Memory size24.7 KiB
2020-12-12T19:58:00.582392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3520
95-th percentile13587.85
Maximum462612
Range462612
Interquartile range (IQR)520

Descriptive statistics

Standard deviation20591.95192
Coefficient of variation (CV)5.293946498
Kurtosis178.9849607
Mean3889.716667
Median Absolute Deviation (MAD)0
Skewness11.51333932
Sum12135916
Variance424028484
MonotocityNot monotonic
2020-12-12T19:58:00.666464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0183158.2%
 
1260.8%
 
2250.8%
 
4130.4%
 
3120.4%
 
8100.3%
 
1390.3%
 
1290.3%
 
580.3%
 
2360.2%
 
960.2%
 
11950.2%
 
2650.2%
 
3450.2%
 
10050.2%
 
2050.2%
 
1050.2%
 
4450.2%
 
650.2%
 
1550.2%
 
12440.1%
 
16540.1%
 
7340.1%
 
2840.1%
 
3840.1%
 
Other values (924)110035.0%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
0183158.2%
 
1260.8%
 
2250.8%
 
3120.4%
 
4130.4%
 
580.3%
 
650.2%
 
730.1%
 
8100.3%
 
960.2%
 
ValueCountFrequency (%) 
4626121< 0.1%
 
3973341< 0.1%
 
3265911< 0.1%
 
2635931< 0.1%
 
2569791< 0.1%
 
2482091< 0.1%
 
2015751< 0.1%
 
1924621< 0.1%
 
19111120.1%
 
1736851< 0.1%
 

Est FY16
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct948
Distinct (%)30.4%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2692.137179
Minimum-18969
Maximum336606
Zeros1736
Zeros (%)55.2%
Memory size24.7 KiB
2020-12-12T19:58:00.753039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-18969
5-th percentile0
Q10
median0
Q3518
95-th percentile10782
Maximum336606
Range355575
Interquartile range (IQR)518

Descriptive statistics

Standard deviation14268.78443
Coefficient of variation (CV)5.300169894
Kurtosis220.1571038
Mean2692.137179
Median Absolute Deviation (MAD)0
Skewness12.7859342
Sum8399468
Variance203598209.1
MonotocityNot monotonic
2020-12-12T19:58:00.836611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0173655.2%
 
2110.3%
 
10100.3%
 
100100.3%
 
5090.3%
 
180.3%
 
50070.2%
 
2570.2%
 
3870.2%
 
20070.2%
 
760.2%
 
460.2%
 
17560.2%
 
660.2%
 
1550.2%
 
5350.2%
 
21350.2%
 
4015150.2%
 
30050.2%
 
2050.2%
 
10350.2%
 
4250.2%
 
51450.2%
 
850.2%
 
34750.2%
 
Other values (923)122939.1%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-189691< 0.1%
 
-152220.1%
 
-1741< 0.1%
 
-1581< 0.1%
 
-1201< 0.1%
 
-811< 0.1%
 
-371< 0.1%
 
-331< 0.1%
 
-251< 0.1%
 
-181< 0.1%
 
ValueCountFrequency (%) 
3366061< 0.1%
 
3001811< 0.1%
 
2258651< 0.1%
 
2129601< 0.1%
 
1969311< 0.1%
 
1582311< 0.1%
 
1543371< 0.1%
 
1314561< 0.1%
 
1302441< 0.1%
 
1206541< 0.1%
 

6 Year Total
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1019
Distinct (%)32.7%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9658.348077
Minimum0
Maximum1548696
Zeros1777
Zeros (%)56.5%
Memory size24.7 KiB
2020-12-12T19:58:00.927689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31800
95-th percentile31884.65
Maximum1548696
Range1548696
Interquartile range (IQR)1800

Descriptive statistics

Standard deviation60022.96174
Coefficient of variation (CV)6.214619857
Kurtosis280.8441034
Mean9658.348077
Median Absolute Deviation (MAD)0
Skewness14.77666905
Sum30134046
Variance3602755936
MonotocityNot monotonic
2020-12-12T19:58:01.015264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0177756.5%
 
200160.5%
 
6000120.4%
 
150110.3%
 
180090.3%
 
30090.3%
 
5090.3%
 
100080.3%
 
300070.2%
 
6070.2%
 
150060.2%
 
120060.2%
 
750060.2%
 
800060.2%
 
200060.2%
 
54060.2%
 
60050.2%
 
2050.2%
 
3300050.2%
 
162050.2%
 
24050.2%
 
110050.2%
 
330050.2%
 
75050.2%
 
27050.2%
 
Other values (994)117437.3%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
0177756.5%
 
140.1%
 
21< 0.1%
 
81< 0.1%
 
91< 0.1%
 
101< 0.1%
 
1220.1%
 
1430.1%
 
151< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
15486961< 0.1%
 
12097151< 0.1%
 
11591591< 0.1%
 
9695541< 0.1%
 
7265441< 0.1%
 
7122361< 0.1%
 
6931761< 0.1%
 
5575741< 0.1%
 
5231561< 0.1%
 
5226331< 0.1%
 

FY 17
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct678
Distinct (%)21.7%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2125.673718
Minimum-6000
Maximum365379
Zeros2014
Zeros (%)64.1%
Memory size24.7 KiB
2020-12-12T19:58:01.108845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-6000
5-th percentile0
Q10
median0
Q3270.75
95-th percentile7252.8
Maximum365379
Range371379
Interquartile range (IQR)270.75

Descriptive statistics

Standard deviation13485.97802
Coefficient of variation (CV)6.344331168
Kurtosis296.3952312
Mean2125.673718
Median Absolute Deviation (MAD)0
Skewness15.00556452
Sum6632102
Variance181871603.2
MonotocityNot monotonic
2020-12-12T19:58:01.192416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0201464.1%
 
50220.7%
 
1000200.6%
 
100190.6%
 
200180.6%
 
300160.5%
 
2000150.5%
 
25110.3%
 
350110.3%
 
40090.3%
 
60080.3%
 
1080.3%
 
25080.3%
 
2070.2%
 
125070.2%
 
6060.2%
 
12060.2%
 
50060.2%
 
15060.2%
 
330050.2%
 
9050.2%
 
1600050.2%
 
7550.2%
 
500050.2%
 
45050.2%
 
Other values (653)87327.8%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-60001< 0.1%
 
-30001< 0.1%
 
0201464.1%
 
140.1%
 
230.1%
 
520.1%
 
620.1%
 
820.1%
 
1080.3%
 
111< 0.1%
 
ValueCountFrequency (%) 
3653791< 0.1%
 
2740301< 0.1%
 
2386161< 0.1%
 
2037511< 0.1%
 
1694771< 0.1%
 
1684901< 0.1%
 
1455211< 0.1%
 
1338041< 0.1%
 
1298811< 0.1%
 
1020001< 0.1%
 

FY 18
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct665
Distinct (%)21.3%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1952.871154
Minimum-6000
Maximum347787
Zeros2066
Zeros (%)65.7%
Memory size24.7 KiB
2020-12-12T19:58:01.281993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-6000
5-th percentile0
Q10
median0
Q3235.25
95-th percentile6624.95
Maximum347787
Range353787
Interquartile range (IQR)235.25

Descriptive statistics

Standard deviation12387.09123
Coefficient of variation (CV)6.343015109
Kurtosis324.4193324
Mean1952.871154
Median Absolute Deviation (MAD)0
Skewness15.59126993
Sum6092958
Variance153440029.2
MonotocityNot monotonic
2020-12-12T19:58:01.366066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0206665.7%
 
100190.6%
 
50150.5%
 
1000130.4%
 
500130.4%
 
300130.4%
 
200120.4%
 
2000110.3%
 
2590.3%
 
1080.3%
 
125070.2%
 
40070.2%
 
4570.2%
 
12560.2%
 
70060.2%
 
60060.2%
 
45060.2%
 
27060.2%
 
55060.2%
 
25060.2%
 
9050.2%
 
17550.2%
 
15050.2%
 
2250.2%
 
1700050.2%
 
Other values (640)85327.1%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-60001< 0.1%
 
-30001< 0.1%
 
0206665.7%
 
240.1%
 
41< 0.1%
 
520.1%
 
620.1%
 
730.1%
 
81< 0.1%
 
1080.3%
 
ValueCountFrequency (%) 
3477871< 0.1%
 
2589881< 0.1%
 
2256991< 0.1%
 
1741651< 0.1%
 
1572311< 0.1%
 
1372691< 0.1%
 
1346641< 0.1%
 
1067851< 0.1%
 
1028581< 0.1%
 
1004201< 0.1%
 

FY 19
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct593
Distinct (%)19.0%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1630.662179
Minimum-6000
Maximum281039
Zeros2176
Zeros (%)69.2%
Memory size24.7 KiB
2020-12-12T19:58:01.454142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-6000
5-th percentile0
Q10
median0
Q3110.5
95-th percentile4804.4
Maximum281039
Range287039
Interquartile range (IQR)110.5

Descriptive statistics

Standard deviation11191.51544
Coefficient of variation (CV)6.863172263
Kurtosis279.3243029
Mean1630.662179
Median Absolute Deviation (MAD)0
Skewness14.99498751
Sum5087666
Variance125250017.9
MonotocityNot monotonic
2020-12-12T19:58:01.536212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0217669.2%
 
300200.6%
 
50170.5%
 
100130.4%
 
1000120.4%
 
200120.4%
 
10100.3%
 
500100.3%
 
200090.3%
 
15080.3%
 
125080.3%
 
25070.2%
 
60070.2%
 
2570.2%
 
400060.2%
 
16060.2%
 
36060.2%
 
27060.2%
 
9060.2%
 
4560.2%
 
45050.2%
 
12550.2%
 
650.2%
 
180050.2%
 
19050.2%
 
Other values (568)74323.6%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-60001< 0.1%
 
-30001< 0.1%
 
0217669.2%
 
230.1%
 
320.1%
 
520.1%
 
650.2%
 
91< 0.1%
 
10100.3%
 
1120.1%
 
ValueCountFrequency (%) 
2810391< 0.1%
 
2286021< 0.1%
 
2121781< 0.1%
 
1782971< 0.1%
 
1683131< 0.1%
 
1470361< 0.1%
 
1291261< 0.1%
 
1226401< 0.1%
 
1081781< 0.1%
 
956801< 0.1%
 

FY 20
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct533
Distinct (%)17.1%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1535.764744
Minimum-1316
Maximum259205
Zeros2292
Zeros (%)72.9%
Memory size24.7 KiB
2020-12-12T19:58:01.622287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1316
5-th percentile0
Q10
median0
Q333.25
95-th percentile5014.5
Maximum259205
Range260521
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation10207.85879
Coefficient of variation (CV)6.646759426
Kurtosis285.8543337
Mean1535.764744
Median Absolute Deviation (MAD)0
Skewness14.96764931
Sum4791586
Variance104200381
MonotocityNot monotonic
2020-12-12T19:58:01.714866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0229272.9%
 
300160.5%
 
1000140.4%
 
50120.4%
 
2000100.3%
 
100100.3%
 
20090.3%
 
50090.3%
 
9090.3%
 
125090.3%
 
1080.3%
 
25070.2%
 
70070.2%
 
400060.2%
 
15060.2%
 
4560.2%
 
660.2%
 
2560.2%
 
150060.2%
 
27050.2%
 
16040.1%
 
3040.1%
 
60040.1%
 
41840.1%
 
35040.1%
 
Other values (508)64720.6%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
-131620.1%
 
0229272.9%
 
230.1%
 
31< 0.1%
 
520.1%
 
660.2%
 
91< 0.1%
 
1080.3%
 
1220.1%
 
141< 0.1%
 
ValueCountFrequency (%) 
2592051< 0.1%
 
2096471< 0.1%
 
2052361< 0.1%
 
1693681< 0.1%
 
1300601< 0.1%
 
1204831< 0.1%
 
1118211< 0.1%
 
970691< 0.1%
 
897911< 0.1%
 
795531< 0.1%
 

FY 21
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)15.7%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1285.091026
Minimum0
Maximum177744
Zeros2384
Zeros (%)75.8%
Memory size24.7 KiB
2020-12-12T19:58:01.802942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4390.6
Maximum177744
Range177744
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7907.592086
Coefficient of variation (CV)6.153332276
Kurtosis229.5568907
Mean1285.091026
Median Absolute Deviation (MAD)0
Skewness13.2775633
Sum4009484
Variance62530012.6
MonotocityNot monotonic
2020-12-12T19:58:01.888015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0238475.8%
 
50150.5%
 
100150.5%
 
300120.4%
 
10110.3%
 
200100.3%
 
1000100.3%
 
125090.3%
 
12570.2%
 
25070.2%
 
15060.2%
 
9060.2%
 
4560.2%
 
2560.2%
 
400060.2%
 
27050.2%
 
600050.2%
 
50050.2%
 
46040.1%
 
850040.1%
 
70040.1%
 
60040.1%
 
200040.1%
 
14030.1%
 
860030.1%
 
Other values (465)56918.1%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
0238475.8%
 
230.1%
 
51< 0.1%
 
620.1%
 
81< 0.1%
 
10110.3%
 
121< 0.1%
 
141< 0.1%
 
151< 0.1%
 
1820.1%
 
ValueCountFrequency (%) 
1777441< 0.1%
 
1731991< 0.1%
 
1447161< 0.1%
 
1146181< 0.1%
 
1058141< 0.1%
 
988661< 0.1%
 
811631< 0.1%
 
797921< 0.1%
 
719301< 0.1%
 
635531< 0.1%
 

FY 22
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct431
Distinct (%)13.8%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1128.284615
Minimum0
Maximum190630
Zeros2454
Zeros (%)78.1%
Memory size24.7 KiB
2020-12-12T19:58:01.981095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3281
Maximum190630
Range190630
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7379.075902
Coefficient of variation (CV)6.540083771
Kurtosis240.5057784
Mean1128.284615
Median Absolute Deviation (MAD)0
Skewness13.46482281
Sum3520248
Variance54450761.17
MonotocityNot monotonic
2020-12-12T19:58:02.069171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0245478.1%
 
300140.4%
 
50130.4%
 
100110.3%
 
200110.3%
 
1000100.3%
 
15090.3%
 
1090.3%
 
125090.3%
 
25080.3%
 
12570.2%
 
2560.2%
 
4560.2%
 
9060.2%
 
27050.2%
 
50050.2%
 
8050.2%
 
46040.1%
 
150040.1%
 
400040.1%
 
300040.1%
 
180040.1%
 
70040.1%
 
2000030.1%
 
33530.1%
 
Other values (406)50216.0%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
0245478.1%
 
230.1%
 
31< 0.1%
 
51< 0.1%
 
630.1%
 
1090.3%
 
121< 0.1%
 
1420.1%
 
151< 0.1%
 
1820.1%
 
ValueCountFrequency (%) 
1906301< 0.1%
 
1256251< 0.1%
 
1189941< 0.1%
 
1175421< 0.1%
 
972941< 0.1%
 
937321< 0.1%
 
858201< 0.1%
 
6753120.1%
 
652821< 0.1%
 
617441< 0.1%
 

Beyond 6 Yrs
Real number (ℝ≥0)

ZEROS

Distinct154
Distinct (%)4.9%
Missing24
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1194.888462
Minimum0
Maximum246835
Zeros2929
Zeros (%)93.2%
Memory size24.7 KiB
2020-12-12T19:58:02.159749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile502
Maximum246835
Range246835
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10647.41339
Coefficient of variation (CV)8.91080108
Kurtosis225.4045121
Mean1194.888462
Median Absolute Deviation (MAD)0
Skewness13.57698914
Sum3728052
Variance113367412
MonotocityNot monotonic
2020-12-12T19:58:02.247324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0292993.2%
 
158640.1%
 
12540.1%
 
10936640.1%
 
5030.1%
 
40030.1%
 
745830.1%
 
653030.1%
 
50130.1%
 
1168420.1%
 
60020.1%
 
78020.1%
 
703120.1%
 
119920.1%
 
4412320.1%
 
77820.1%
 
5259420.1%
 
969020.1%
 
23220.1%
 
153620.1%
 
121620.1%
 
24020.1%
 
1705820.1%
 
795020.1%
 
52120.1%
 
Other values (129)1324.2%
 
(Missing)240.8%
 
ValueCountFrequency (%) 
0292993.2%
 
111< 0.1%
 
211< 0.1%
 
401< 0.1%
 
5030.1%
 
521< 0.1%
 
591< 0.1%
 
801< 0.1%
 
851< 0.1%
 
911< 0.1%
 
ValueCountFrequency (%) 
2468351< 0.1%
 
2245731< 0.1%
 
1670721< 0.1%
 
1476351< 0.1%
 
1346101< 0.1%
 
1316681< 0.1%
 
1219901< 0.1%
 
10936640.1%
 
970301< 0.1%
 
878061< 0.1%
 

Interactions

2020-12-12T19:57:49.287172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.376749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.460321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.546895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.634971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.719544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.803616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.886688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:49.968758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.053331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.142408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.224478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.301044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.372105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.450673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.529741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.607308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.683874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.756436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.829999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.908067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:50.986134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.059697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.146271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.224839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.309912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.399990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.485563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.569636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.654709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.735779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.818850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.905425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:51.985994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.072569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.157141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.244216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.332292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.421369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.510445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.595519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.679091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.765665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.853741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:52.942817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.026390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.105958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.189029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.275604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.357675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.442247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.523317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.603886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.687959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.773032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.854602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:53.940676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.020745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.105818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.191892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.275464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.359036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.439105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.520175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.603246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.689321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.770390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.848958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:54.922521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.002090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.082659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.161727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.239794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.314859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.389423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.469492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.550061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.626627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.705695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.779258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.857326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:55.938396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.016963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.096531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.172096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.246661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.324728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.405297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.484365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.568938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.648507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.732078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.817652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.900723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:56.984796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.064364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.143933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.226003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.310576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.391145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.479722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.561792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.648867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.738444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.825019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.910593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:57.994665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.077736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.164311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.253387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.335958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.416027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.493094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.574664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.657235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.739806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.819375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.900945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:58.978512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:59.058080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:59.138149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:58:02.327393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:58:02.451000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:58:02.576608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:58:02.703217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-12T19:57:59.302290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:59.469934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:59.622065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:57:59.772195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

ProjectCost ElementTotalThru FY15Est FY166 Year TotalFY 17FY 18FY 19FY 20FY 21FY 22Beyond 6 Yrs
0Council Office Building Renovations (P010100)Planning, Design and Supervision2553.0669.0334.01550.01000.0550.00.00.00.00.00.0
1Council Office Building Renovations (P010100)Land4.04.00.00.00.00.00.00.00.00.00.0
2Council Office Building Renovations (P010100)Site Improvements and Utilities2.02.00.00.00.00.00.00.00.00.00.0
3Council Office Building Renovations (P010100)Construction36414.03272.0293.032849.019785.013064.00.00.00.00.00.0
4Council Office Building Renovations (P010100)Other1218.08.00.01210.0610.0600.00.00.00.00.00.0
5Council Office Building Renovations (P010100)Total Expenditures40191.03955.0627.035609.021395.014214.00.00.00.00.00.0
6Council Office Building Garage (P011601)Planning, Design and Supervision875.00.0159.0716.0395.0263.058.00.00.00.00.0
7Council Office Building Garage (P011601)Land0.00.00.00.00.00.00.00.00.00.00.0
8Council Office Building Garage (P011601)Site Improvements and Utilities0.00.00.00.00.00.00.00.00.00.00.0
9Council Office Building Garage (P011601)Construction3884.00.00.03884.01748.02136.00.00.00.00.00.0

Last rows

ProjectCost ElementTotalThru FY15Est FY166 Year TotalFY 17FY 18FY 19FY 20FY 21FY 22Beyond 6 Yrs
3134Mass Transit (SC96)Site Improvements and Utilities0.00.00.00.00.00.00.00.00.00.00.0
3135Mass Transit (SC96)Construction5294.00.05294.00.00.00.00.00.00.00.00.0
3136Mass Transit (SC96)Other0.00.00.00.00.00.00.00.00.00.00.0
3137Mass Transit (SC96)Total Expenditures5317.00.05317.00.00.00.00.00.00.00.00.0
3138WMATAPlanning, Design and Supervision23.00.023.00.00.00.00.00.00.00.00.0
3139WMATALand0.00.00.00.00.00.00.00.00.00.00.0
3140WMATASite Improvements and Utilities0.00.00.00.00.00.00.00.00.00.00.0
3141WMATAConstruction5294.00.05294.00.00.00.00.00.00.00.00.0
3142WMATAOther0.00.00.00.00.00.00.00.00.00.00.0
3143WMATATotal Expenditures5317.00.05317.00.00.00.00.00.00.00.00.0